{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# week one homeWork\n",
    "# Part One EDA Data\n",
    "# Data description: continues and category features\n",
    "# part-one:EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# data manipulation \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# plotting\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (30, 10),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "sn.set_style('whitegrid')\n",
    "sn.set_context('talk')\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "# pandas display data frames as tables\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data from file\n",
    "train = pd.read_csv(\"day.csv\")\n",
    "train.head()\n",
    "# print data shape of training Data\n",
    "# print(\"train : \" + str(train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# input data info file\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Bikeshare数据集上的数据探索\t字段说明\n",
    "# Instant记录号\n",
    "# Dteday：日期\n",
    "# Season：季节（1=春天、2=夏天、3=秋天、4=冬天）(离散)\n",
    "# yr：年份，(0: 2011, 1:2012)  (离散)\n",
    "# mnth：月份( 1 to 12) (离散)\n",
    "# hr：小时 (0 to 23)  （只在hour.csv有，作业忽略此字段）\n",
    "# holiday：是否是节假日 (离散)\n",
    "# weekday：星期中的哪天，取值为0～6 (离散) \n",
    "# workingday：是否工作日 (离散)\n",
    "# 1=工作日 （是否为工作日，1为工作日，0为非周末或节假日 \n",
    "# weathersit：天气（1：晴天，多云 ",
    "2：雾天，阴天 ",
    "3：小雪，小雨 ",
    "4：大雨，大雪，大雾）(离散)\n",
    "\n",
    "# temp：气温摄氏度\n",
    "# atemp：体感温度\n",
    "# hum：湿度\n",
    "# windspeed：风速\n",
    "# casual：非注册用户个数\n",
    "# registered：注册用户个数\n",
    "# cnt：给定日期（天）时间（每小时）总租车人数，响应变量y （cnt = casual + registered）\n",
    "# casual、registered和cnt三个特征均为要预测的y，作业里只需对cnt进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# featureEDA:\n",
    "# 1) Categorical variables\n",
    "# 2) Numeric variables\n",
    "# 3) Numeric feature with numerical response\n",
    "# 4) Categorical feature with numerical response\n",
    "# 5) Numeric feature with categorical response\n",
    "# 6) Categorical feature with categorical response\n",
    "# Categorical features:\n",
    "# Season：季节（1=春天、2=夏天、3=秋天、4=冬天）, holiday, weekday, workingday, weathersit, mnth\n",
    "# Numeric variables:\n",
    "# temp,atemp,hum,windspeed,casual,registered\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "season features counts\n",
      "3    188\n",
      "2    184\n",
      "1    181\n",
      "4    178\n",
      "Name: season, dtype: int64\n",
      "\n",
      "holiday features counts\n",
      "0    710\n",
      "1     21\n",
      "Name: holiday, dtype: int64\n",
      "\n",
      "weekday features counts\n",
      "6    105\n",
      "1    105\n",
      "0    105\n",
      "5    104\n",
      "4    104\n",
      "3    104\n",
      "2    104\n",
      "Name: weekday, dtype: int64\n",
      "\n",
      "workingday features counts\n",
      "1    500\n",
      "0    231\n",
      "Name: workingday, dtype: int64\n",
      "\n",
      "weathersit features counts\n",
      "1    463\n",
      "2    247\n",
      "3     21\n",
      "Name: weathersit, dtype: int64\n",
      "\n",
      "mnth features counts\n",
      "12    62\n",
      "10    62\n",
      "8     62\n",
      "7     62\n",
      "5     62\n",
      "3     62\n",
      "1     62\n",
      "11    60\n",
      "9     60\n",
      "6     60\n",
      "4     60\n",
      "2     57\n",
      "Name: mnth, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# part one : category features and response\n",
    "categorical_features = ['season','holiday','weekday','workingday','weathersit','mnth']\n",
    "for col in categorical_features:\n",
    "    print '\\n%s features counts'%col\n",
    "    print train[col].value_counts()\n",
    "    train[col] = train[col].astype('object')   \n",
    "# category features for hist picture    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'Season wise hourly distribution of counts')]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FixZcuXKFtWvXcuHCBZo2bcq8efPo2LHjQ7ySt1a8lCxZEkj/Cj/zKzEh61dg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\n9ddfZ//+/UycOJGJEyfi5uZ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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for each features need to find the influence of feature and response\n",
    "# season for cnt response \n",
    "fig,ax = plt.subplots()\n",
    "sn.pointplot(data=train[['season','holiday','weekday','workingday','weathersit','mnth','cnt']],x='season',y='cnt',hue='season',ax=ax)\n",
    "ax.set(title=\"Season wise hourly distribution of counts\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'holiday distribution of counts')]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for each features need to find the influence of feature and response\n",
    "# holiday for cnt response \n",
    "fig,ax = plt.subplots()\n",
    "sn.pointplot(data=train[['season','holiday','weekday','workingday','weathersit','mnth','cnt']],x='holiday',y='cnt',hue='season',ax=ax)\n",
    "ax.set(title=\"holiday distribution of counts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'weekday wise hourly distribution of counts')]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for each features need to find the influence of feature and response\n",
    "# holiday for cnt response \n",
    "fig,ax = plt.subplots()\n",
    "sn.pointplot(data=train[['season','holiday','weekday','workingday','weathersit','mnth','cnt']],x='weekday',y='cnt',hue='weekday',ax=ax)\n",
    "ax.set(title=\"weekday wise hourly distribution of counts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'workingday wise hourly distribution of counts')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for each features need to find the influence of feature and response\n",
    "# holiday for cnt response \n",
    "fig,ax = plt.subplots()\n",
    "sn.pointplot(data=train[['season','holiday','weekday','workingday','weathersit','mnth','cnt']],x='workingday',y='cnt',hue='workingday',ax=ax)\n",
    "ax.set(title=\"workingday wise hourly distribution of counts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'mnth wise hourly distribution of counts')]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for each features need to find the influence of feature and response\n",
    "# holiday for cnt response \n",
    "fig,ax = plt.subplots()\n",
    "sn.pointplot(data=train[['season','holiday','weekday','workingday','weathersit','mnth','cnt']],x='mnth',y='cnt',hue='mnth',ax=ax)\n",
    "ax.set(title=\"mnth wise hourly distribution of counts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'mnth wise hourly distribution of counts')]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for each features need to find the influence of feature and response\n",
    "# holiday for cnt response \n",
    "fig,ax = plt.subplots()\n",
    "sn.pointplot(data=train[['season','holiday','weekday','workingday','weathersit','mnth','cnt','yr']],x='yr',y='cnt',hue='yr',ax=ax)\n",
    "ax.set(title=\"mnth wise hourly distribution of counts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#for above ana:\n",
    "#season','holiday','weekday','workingday','weathersit','mnth','cnt'\n",
    "#weathersit for cnt to make sure\n",
    "#season, weekday, workingday, mnth have very influence on label cnt except weathersit, holiday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# part two : numeric variables and response\n",
    "# numeric variable: calculate mean, sd, median, quantile, min, max, \n",
    "# correlation for numeric variable\n",
    "# numeric variable and response\n",
    "## Bikeshare数据集上的数据探索\t字段说明\n",
    "# Instant记录号\n",
    "# Dteday：日期\n",
    "# Season：季节（1=春天、2=夏天、3=秋天、4=冬天）\n",
    "# yr：年份，(0: 2011, 1:2012)\n",
    "# mnth：月份( 1 to 12)\n",
    "# hr：小时 (0 to 23)  （只在hour.csv有，作业忽略此字段）\n",
    "# holiday：是否是节假日\n",
    "# weekday：星期中的哪天，取值为0～6\n",
    "# workingday：是否工作日\n",
    "# 1=工作日 （是否为工作日，1为工作日，0为非周末或节假日\n",
    "# weathersit：天气（1：晴天，多云 ",
    "2：雾天，阴天 ",
    "3：小雪，小雨 ",
    "4：大雨，大雪，大雾）\n",
    "# temp：气温摄氏度\n",
    "# atemp：体感温度\n",
    "# hum：湿度\n",
    "# windspeed：风速\n",
    "# casual：非注册用户个数\n",
    "# registered：注册用户个数\n",
    "# cnt：给定日期（天）时间（每小时）总租车人数，响应变量y （cnt = casual + registered）\n",
    "# casual、registered和cnt三个特征均为要预测的y，作业里只需对cnt进行预测\n",
    "# Dteday,yr,temp,atemp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numericTable = train.drop(['instant', 'season', 'yr', 'mnth', 'holiday', 'weekday', 'workingday', 'weathersit'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             temp       atemp         hum   windspeed       casual  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000   731.000000   \n",
       "mean     0.495385    0.474354    0.627894    0.190486   848.176471   \n",
       "std      0.183051    0.162961    0.142429    0.077498   686.622488   \n",
       "min      0.059130    0.079070    0.000000    0.022392     2.000000   \n",
       "25%      0.337083    0.337842    0.520000    0.134950   315.500000   \n",
       "50%      0.498333    0.486733    0.626667    0.180975   713.000000   \n",
       "75%      0.655417    0.608602    0.730209    0.233214  1096.000000   \n",
       "max      0.861667    0.840896    0.972500    0.507463  3410.000000   \n",
       "\n",
       "        registered          cnt  \n",
       "count   731.000000   731.000000  \n",
       "mean   3656.172367  4504.348837  \n",
       "std    1560.256377  1937.211452  \n",
       "min      20.000000    22.000000  \n",
       "25%    2497.000000  3152.000000  \n",
       "50%    3662.000000  4548.000000  \n",
       "75%    4776.500000  5956.000000  \n",
       "max    6946.000000  8714.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numericTable.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dteday and temp = 0.99\n",
      "casual and registered = 0.95\n",
      "windspeed and registered = 0.67\n",
      "temp and registered = 0.63\n",
      "dteday and registered = 0.63\n",
      "temp and casual = 0.54\n",
      "temp and windspeed = 0.54\n",
      "dteday and windspeed = 0.54\n",
      "dteday and casual = 0.54\n"
     ]
    }
   ],
   "source": [
    "# correlation for numeric variable\n",
    "cols = numericTable.columns\n",
    "data_corr = numericTable.corr()\n",
    "data_corr = data_corr.abs()\n",
    "# plt.subplots(figize = (13,9))\n",
    "# sns.heatmap(data_corr, annot = True)\n",
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0aae9d90>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VAQAAAMgQGzZsUI8ePeTj46Py5curbt26On/+vMaOHavx48enu5+1a9eqc+fO2rRpk0qU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\nUY4AAAAAQJL0FyKiwJ7rAHuBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2160x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# numeric variable and response\n",
    "# temp\tatemp\thum--windspeed\tcasual\tregistered\tcnt\n",
    "corrMatt = numericTable[[\"temp\",\"atemp\",\n",
    "                    \"hum\",\"windspeed\",\n",
    "                    \"casual\",\"registered\",\n",
    "                    \"cnt\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sn.heatmap(corrMatt, mask=mask,\n",
    "           vmax=.8, square=True,annot=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 因为相关性会影响模型的训练效果\n",
    "# dteday and temp = 0.99\n",
    "# casual and registered = 0.95\n",
    "# 所以对于casual and registered 来说相互之间的相关性比较高，从casual 和 registered 对于cnt y 来说，\n",
    "# registered 对于cnt的相关性更强一些."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0ab38fd0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KS19fH6GhodDQ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\n",
      "text/plain": [
       "<Figure size 2160x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# temp\tatemp\thum--windspeed\tcasual\tregistered\tcnt\n",
    "fig,(ax1,ax2)= plt.subplots(ncols=2)\n",
    "sn.boxplot(data=numericTable[['temp',\n",
    "                         'atemp','hum']],ax=ax1)\n",
    "sn.boxplot(data=numericTable[['windspeed','casual','registered']],ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 对于连续的特征来说，可以做boxplot的对于特征的分析，可以看出min,max,Q1,med,Q3,如果对于特征来说，有些特征的值大于max或者小于min\n",
    "# 有异常值\n",
    "# casual val 有异常的值－可能需要对casual做处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 综上\n",
    "# 离散的特征\n",
    "# season,holiday,weekday,workingday,weathersit,mnth,cnt,yr;离散特征holiday(对于label的影响比较小,暂时删除)\n",
    "# 连续特征\n",
    "# temp\tatemp\thum \t windspeed\tcasual(异常值,暂时删除)\tregistered\tcnt\n",
    "# season,weekday,workingday,weathersit,mnth,cnt,yr,temp,atemp,hum,windspeed,registered,cnt\n",
    "#对类别型特征，观察其取值范围及直方图\n",
    "## Bikeshare数据集上的数据探索\t字段说明\n",
    "# Instant记录号\n",
    "# Dteday：日期\n",
    "# Season;yr;mnth;holiday;weekday;workingday;weathersit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data from file\n",
    "train = pd.read_csv(\"day.csv\")\n",
    "train.head()\n",
    "# print data shape of training Data\n",
    "# print(\"train : \" + str(train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>...</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>mnth_7</th>\n",
       "      <th>mnth_8</th>\n",
       "      <th>mnth_9</th>\n",
       "      <th>mnth_10</th>\n",
       "      <th>mnth_11</th>\n",
       "      <th>mnth_12</th>\n",
       "      <th>yr_0</th>\n",
       "      <th>yr_1</th>\n",
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       "      <th>3</th>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  weekday_0  weekday_1  weekday_2  \\\n",
       "0         1         0         0         0          0          0          0   \n",
       "1         1         0         0         0          1          0          0   \n",
       "2         1         0         0         0          0          1          0   \n",
       "3         1         0         0         0          0          0          1   \n",
       "4         1         0         0         0          0          0          0   \n",
       "\n",
       "   weekday_3  weekday_4  weekday_5  ...   mnth_5  mnth_6  mnth_7  mnth_8  \\\n",
       "0          0          0          0  ...        0       0       0       0   \n",
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       "3          0          0          0  ...        0       0       0       0   \n",
       "4          1          0          0  ...        0       0       0       0   \n",
       "\n",
       "   mnth_9  mnth_10  mnth_11  mnth_12  yr_0  yr_1  \n",
       "0       0        0        0        0     1     0  \n",
       "1       0        0        0        0     1     0  \n",
       "2       0        0        0        0     1     0  \n",
       "3       0        0        0        0     1     0  \n",
       "4       0        0        0        0     1     0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model file pre for category features\n",
    "categorical_features = ['season','weekday','workingday','weathersit','mnth','yr']\n",
    "\n",
    "#数据类型变为object，才能被get_dummies处理\n",
    "for col in categorical_features:\n",
    "    train[col] = train[col].astype('object')\n",
    "    \n",
    "X_train_cat = train[categorical_features]\n",
    "X_train_cat = pd.get_dummies(X_train_cat)\n",
    "X_train_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>-0.753734</td>\n",
       "      <td>-1.925471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.634657</td>\n",
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       "      <td>0.746632</td>\n",
       "      <td>-1.061246</td>\n",
       "      <td>-1.556689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.614780</td>\n",
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       "      <td>-0.389829</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp     atemp       hum  windspeed    casual  registered\n",
       "0 -0.826662 -0.679946  1.250171  -0.387892 -0.753734   -1.925471\n",
       "1 -0.721095 -0.740652  0.479113   0.749602 -1.045214   -1.915209\n",
       "2 -1.634657 -1.749767 -1.339274   0.746632 -1.061246   -1.556689\n",
       "3 -1.614780 -1.610270 -0.263182  -0.389829 -1.078734   -1.412383\n",
       "4 -1.467414 -1.504971 -1.341494  -0.046307 -1.116627   -1.371336"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model file pre for category features\n",
    "#数值型变量预处理，\n",
    "# 数据标准化\n",
    "#temp\tatemp\thum \t windspeed\tcasual(异常值,暂时删除)\tregistered\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "mn_X = StandardScaler()\n",
    "mn_y = StandardScaler()\n",
    "numerical_features = ['temp','atemp','hum','windspeed','casual','registered']\n",
    "temp = mn_X.fit_transform(train[numerical_features])\n",
    "\n",
    "X_train_num = pd.DataFrame(data=temp, columns=numerical_features, index =train.index)\n",
    "X_train_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/DalinXie/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:3: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "#对y做标准化\n",
    "y = train['cnt']\n",
    "y = mn_y.fit_transform(y.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <td>1</td>\n",
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       "      <td>-1.500269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  weekday_0  weekday_1  weekday_2  \\\n",
       "0         1         0         0         0          0          0          0   \n",
       "1         1         0         0         0          1          0          0   \n",
       "2         1         0         0         0          0          1          0   \n",
       "3         1         0         0         0          0          0          1   \n",
       "4         1         0         0         0          0          0          0   \n",
       "\n",
       "   weekday_3  weekday_4  weekday_5    ...     mnth_12  yr_0  yr_1      temp  \\\n",
       "0          0          0          0    ...           0     1     0 -0.826662   \n",
       "1          0          0          0    ...           0     1     0 -0.721095   \n",
       "2          0          0          0    ...           0     1     0 -1.634657   \n",
       "3          0          0          0    ...           0     1     0 -1.614780   \n",
       "4          1          0          0    ...           0     1     0 -1.467414   \n",
       "\n",
       "      atemp       hum  windspeed    casual  registered       cnt  \n",
       "0 -0.679946  1.250171  -0.387892 -0.753734   -1.925471 -1.817953  \n",
       "1 -0.740652  0.479113   0.749602 -1.045214   -1.915209 -1.912999  \n",
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       "3 -1.610270 -0.263182  -0.389829 -1.078734   -1.412383 -1.519898  \n",
       "4 -1.504971 -1.341494  -0.046307 -1.116627   -1.371336 -1.500269  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#合并x and y\n",
    "X_train = pd.concat([X_train_cat, X_train_num], axis = 1, ignore_index=False)\n",
    "X_train[\"cnt\"] = y\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 36 columns):\n",
      "season_1        731 non-null uint8\n",
      "season_2        731 non-null uint8\n",
      "season_3        731 non-null uint8\n",
      "season_4        731 non-null uint8\n",
      "weekday_0       731 non-null uint8\n",
      "weekday_1       731 non-null uint8\n",
      "weekday_2       731 non-null uint8\n",
      "weekday_3       731 non-null uint8\n",
      "weekday_4       731 non-null uint8\n",
      "weekday_5       731 non-null uint8\n",
      "weekday_6       731 non-null uint8\n",
      "workingday_0    731 non-null uint8\n",
      "workingday_1    731 non-null uint8\n",
      "weathersit_1    731 non-null uint8\n",
      "weathersit_2    731 non-null uint8\n",
      "weathersit_3    731 non-null uint8\n",
      "mnth_1          731 non-null uint8\n",
      "mnth_2          731 non-null uint8\n",
      "mnth_3          731 non-null uint8\n",
      "mnth_4          731 non-null uint8\n",
      "mnth_5          731 non-null uint8\n",
      "mnth_6          731 non-null uint8\n",
      "mnth_7          731 non-null uint8\n",
      "mnth_8          731 non-null uint8\n",
      "mnth_9          731 non-null uint8\n",
      "mnth_10         731 non-null uint8\n",
      "mnth_11         731 non-null uint8\n",
      "mnth_12         731 non-null uint8\n",
      "yr_0            731 non-null uint8\n",
      "yr_1            731 non-null uint8\n",
      "temp            731 non-null float64\n",
      "atemp           731 non-null float64\n",
      "hum             731 non-null float64\n",
      "windspeed       731 non-null float64\n",
      "registered      731 non-null float64\n",
      "cnt             731 non-null float64\n",
      "dtypes: float64(6), uint8(30)\n",
      "memory usage: 55.8 KB\n"
     ]
    }
   ],
   "source": [
    "# drop other feature with outliter; casual;\n",
    "fin_data_train = X_train.drop(['casual'], axis=1)\n",
    "fin_data_train.head()\n",
    "fin_data_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save data file \n",
    "fin_data_train.to_csv('DalinXie_Practice.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>season_2</th>\n",
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       "      <td>-1.556689</td>\n",
       "      <td>-1.629925</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>-1.412383</td>\n",
       "      <td>-1.519898</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>-1.371336</td>\n",
       "      <td>-1.500269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  weekday_0  weekday_1  weekday_2  \\\n",
       "0         1         0         0         0          0          0          0   \n",
       "1         1         0         0         0          1          0          0   \n",
       "2         1         0         0         0          0          1          0   \n",
       "3         1         0         0         0          0          0          1   \n",
       "4         1         0         0         0          0          0          0   \n",
       "\n",
       "   weekday_3  weekday_4  weekday_5    ...     mnth_11  mnth_12  yr_0  yr_1  \\\n",
       "0          0          0          0    ...           0        0     1     0   \n",
       "1          0          0          0    ...           0        0     1     0   \n",
       "2          0          0          0    ...           0        0     1     0   \n",
       "3          0          0          0    ...           0        0     1     0   \n",
       "4          1          0          0    ...           0        0     1     0   \n",
       "\n",
       "       temp     atemp       hum  windspeed  registered       cnt  \n",
       "0 -0.826662 -0.679946  1.250171  -0.387892   -1.925471 -1.817953  \n",
       "1 -0.721095 -0.740652  0.479113   0.749602   -1.915209 -1.912999  \n",
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       "4 -1.467414 -1.504971 -1.341494  -0.046307   -1.371336 -1.500269  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin_data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>5 rows × 36 columns</p>\n",
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      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  weekday_0  weekday_1  weekday_2  \\\n",
       "0         1         0         0         0          0          0          0   \n",
       "1         1         0         0         0          1          0          0   \n",
       "2         1         0         0         0          0          1          0   \n",
       "3         1         0         0         0          0          0          1   \n",
       "4         1         0         0         0          0          0          0   \n",
       "\n",
       "   weekday_3  weekday_4  weekday_5    ...     mnth_11  mnth_12  yr_0  yr_1  \\\n",
       "0          0          0          0    ...           0        0     1     0   \n",
       "1          0          0          0    ...           0        0     1     0   \n",
       "2          0          0          0    ...           0        0     1     0   \n",
       "3          0          0          0    ...           0        0     1     0   \n",
       "4          1          0          0    ...           0        0     1     0   \n",
       "\n",
       "       temp     atemp       hum  windspeed  registered       cnt  \n",
       "0 -0.826662 -0.679946  1.250171  -0.387892   -1.925471 -1.817953  \n",
       "1 -0.721095 -0.740652  0.479113   0.749602   -1.915209 -1.912999  \n",
       "2 -1.634657 -1.749767 -1.339274   0.746632   -1.556689 -1.629925  \n",
       "3 -1.614780 -1.610270 -0.263182  -0.389829   -1.412383 -1.519898  \n",
       "4 -1.467414 -1.504971 -1.341494  -0.046307   -1.371336 -1.500269  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model training data set\n",
    "train_data = pd.read_csv(\"DalinXie_Practice.csv\")\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 3. 对全体数据，随机选择其中80%做训练数据，剩下20%为测试数据，评价指标为RMSE\n",
    "# 4. 用训练数据训练最小二乘线性回归模型、岭回归模型、Lasso模型，其中岭回归模型 和Lasso模型,注意岭回归模型和Lasso模型的正则超参数调优。 \n",
    "# 5. 比较用上述三种模型得到的各特征的系数，以及各模型在测试集上的性能。并简单说明原因。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model-1:\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 36)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731,)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = train_data[\"cnt\"]\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(train_data, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.00\n"
     ]
    }
   ],
   "source": [
    "clf = linear_model.LinearRegression(normalize = True, n_jobs = 15)\n",
    "# Create linear regression object\n",
    "# Train the model using the training sets\n",
    "clf.fit(X_train, Y_train)\n",
    "# Make predictions using the testing set\n",
    "diabetes_y_pred = clf.predict(X_test)\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(Y_test, diabetes_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#model 2 : Ridge\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
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       "      <th>mnth_12</th>\n",
       "      <th>yr_0</th>\n",
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       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
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       "      <td>0.746632</td>\n",
       "      <td>-1.556689</td>\n",
       "      <td>-1.629925</td>\n",
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       "      <td>-0.046307</td>\n",
       "      <td>-1.371336</td>\n",
       "      <td>-1.500269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  weekday_0  weekday_1  weekday_2  \\\n",
       "0         1         0         0         0          0          0          0   \n",
       "1         1         0         0         0          1          0          0   \n",
       "2         1         0         0         0          0          1          0   \n",
       "3         1         0         0         0          0          0          1   \n",
       "4         1         0         0         0          0          0          0   \n",
       "\n",
       "   weekday_3  weekday_4  weekday_5    ...     mnth_11  mnth_12  yr_0  yr_1  \\\n",
       "0          0          0          0    ...           0        0     1     0   \n",
       "1          0          0          0    ...           0        0     1     0   \n",
       "2          0          0          0    ...           0        0     1     0   \n",
       "3          0          0          0    ...           0        0     1     0   \n",
       "4          1          0          0    ...           0        0     1     0   \n",
       "\n",
       "       temp     atemp       hum  windspeed  registered       cnt  \n",
       "0 -0.826662 -0.679946  1.250171  -0.387892   -1.925471 -1.817953  \n",
       "1 -0.721095 -0.740652  0.479113   0.749602   -1.915209 -1.912999  \n",
       "2 -1.634657 -1.749767 -1.339274   0.746632   -1.556689 -1.629925  \n",
       "3 -1.614780 -1.610270 -0.263182  -0.389829   -1.412383 -1.519898  \n",
       "4 -1.467414 -1.504971 -1.341494  -0.046307   -1.371336 -1.500269  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv(\"DalinXie_Practice.csv\")\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = train_data[\"cnt\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731,)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(train_data, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.02\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.1, 'normalize': 'True', 'solver': 'sparse_cg', 'tol': 0.0001}"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {'alpha':[0.1, 1], \n",
    "              'normalize':('True','False'), \n",
    "              'tol':[0.0001, 0.1],\n",
    "              'solver':('svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga')\n",
    "             }\n",
    "Ridge = linear_model.Ridge()\n",
    "# Create linear regression object\n",
    "# Train the model using the training sets\n",
    "# Make predictions using the testing set\n",
    "clf = GridSearchCV(Ridge, parameters)\n",
    "clf.fit(X_train,Y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(Y_test, y_pred))\n",
    "# print clf.cv_results_\n",
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.00\n"
     ]
    }
   ],
   "source": [
    "clf = linear_model.Ridge(alpha = 0.1, normalize = False, solver = 'sag', tol = 0.0001)\n",
    "# Create linear regression object\n",
    "# Train the model using the training sets\n",
    "clf.fit(X_train, Y_train)\n",
    "# Make predictions using the testing set\n",
    "diabetes_y_pred = clf.predict(X_test)\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(Y_test, diabetes_y_pred))\n",
    "# Plot outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#model 3 : Lasso\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>-1.371336</td>\n",
       "      <td>-1.500269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  weekday_0  weekday_1  weekday_2  \\\n",
       "0         1         0         0         0          0          0          0   \n",
       "1         1         0         0         0          1          0          0   \n",
       "2         1         0         0         0          0          1          0   \n",
       "3         1         0         0         0          0          0          1   \n",
       "4         1         0         0         0          0          0          0   \n",
       "\n",
       "   weekday_3  weekday_4  weekday_5    ...     mnth_11  mnth_12  yr_0  yr_1  \\\n",
       "0          0          0          0    ...           0        0     1     0   \n",
       "1          0          0          0    ...           0        0     1     0   \n",
       "2          0          0          0    ...           0        0     1     0   \n",
       "3          0          0          0    ...           0        0     1     0   \n",
       "4          1          0          0    ...           0        0     1     0   \n",
       "\n",
       "       temp     atemp       hum  windspeed  registered       cnt  \n",
       "0 -0.826662 -0.679946  1.250171  -0.387892   -1.925471 -1.817953  \n",
       "1 -0.721095 -0.740652  0.479113   0.749602   -1.915209 -1.912999  \n",
       "2 -1.634657 -1.749767 -1.339274   0.746632   -1.556689 -1.629925  \n",
       "3 -1.614780 -1.610270 -0.263182  -0.389829   -1.412383 -1.519898  \n",
       "4 -1.467414 -1.504971 -1.341494  -0.046307   -1.371336 -1.500269  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv(\"DalinXie_Practice.csv\")\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.01\n",
      "{'alpha': 0.1, 'max_iter': 1000, 'tol': 0.0001}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/DalinXie/anaconda2/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.98992492510730667"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {'alpha':[0.1,1], \n",
    "              'max_iter':[10, 1000],\n",
    "              'tol':[0.0001, 10]\n",
    "             }\n",
    "Lasso = linear_model.Lasso()\n",
    "# Create linear regression object\n",
    "# Train the model using the training sets\n",
    "# Make predictions using the testing set\n",
    "clf = GridSearchCV(Lasso, parameters)\n",
    "clf.fit(X_train,Y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(Y_test, y_pred))\n",
    "# print clf.cv_results_\n",
    "print clf.best_params_\n",
    "clf.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.01\n"
     ]
    }
   ],
   "source": [
    "clf = linear_model.Lasso(alpha = 0.1,max_iter = 1000, tol = 0.0001)\n",
    "# Create linear regression object\n",
    "# Train the model using the training sets\n",
    "clf.fit(X_train, Y_train)\n",
    "# Make predictions using the testing set\n",
    "diabetes_y_pred = clf.predict(X_test)\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(Y_test, diabetes_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# model one: LinearRegression\n",
    "# model two: Ridge\n",
    "# model three: Lasso\n",
    "# 用训练数据训练最小二乘线性回归模型: Mean squared error: 0.00\n",
    "# 岭回归模型: Mean squared error: 0.02, 最佳参数: {'alpha': 0.1, 'normalize': 'True', 'solver': 'sparse_cg', 'tol': 0.0001}\n",
    "# Lasso模型: Mean squared error: 0.01 最佳参数 {'alpha': 0.1, 'max_iter': 1000, 'tol': 0.0001}\n",
    "# 分别用 GridSearchCV 对模型参数进行调优\n",
    "# 在测试集的表现，接近100%\n",
    "# 可能会有过拟合问题"
   ]
  }
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